<p>Long-term exposure to ambient ozone (O<sub>3</sub>) is associated with a variety of impacts, including adverse human-health effects and reduced yields in commercial crops. Ground-level O<sub>3</sub> concentrations for assessments are typically predicted using chemical transport models, however such methods often feature biases that can influence impact estimates. Here, we develop and apply artificial neural networks to empirically model long-term O<sub>3</sub> exposure over the continental United States from 2000&ndash;2015, and generate a measurement-based assessment of impacts on human-health and crop yields. Notably, we find that two commonly-used human-health averaging metrics, based on separate epidemiological studies, differ in their trends over the study period. The population-weighted, April&ndash;September average of the daily 1-hour maximum concentration peaked in 2002 at 55.9&thinsp;ppb and decreased by &minus;0.43 [95&thinsp;% CI: &minus;0.28, &minus;0.57]&thinsp;ppb/yr between 2000&ndash;2015, yielding a ~&thinsp;18&thinsp;% decrease in normalized human-health impacts. In contrast, there was little change in the population-weighted, annual average of the maximum daily 8-hour average concentration between 2000&ndash;2015, which resulted in a ~&thinsp;5&thinsp;% increase in normalized human-health impacts. In both cases, an aging population structure played a substantial role in modulating these trends. By contrast, all agriculture-weighted crop-loss metrics featured decreasing trends, leading to reductions in the estimated national relative yield loss ranging from 1.7&ndash;1.9&thinsp;% for maize, 5.1&ndash;7.1&thinsp;% for soybeans, and 2.7&thinsp;% for wheat. Overall, these results provide a measurement-based estimate of long-term O<sub>3</sub> exposure over the United States, quantify the historical magnitude, trends, and impacts of such exposure, and illustrate how different conclusions regarding historical impacts can be made through the use of varying metrics.</p>